{
 "metadata": {
  "name": "ch2"
 }, 
 "nbformat": 2, 
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "''''", 
      "-------------------------------------------------------------------------------", 
      "Filename   : ch2.ipynb", 
      "Date       : 2012-04-30", 
      "Author     : C. Vogel", 
      "Purpose    : Replicate analysis of height and weight data in Chapter 2 of ", 
      "           : _Machine Learning for Hackers_.", 
      "Input Data : 01_heights_weights_genders.tsv is available at the book's github ", 
      "           : repository at https://github.com/johnmyleswhite/ML_for_Hackers.git", 
      "Libraries  : Numpy 1.6.1, Matplotlib 1.1.0, Pandas 0.7.3, scipy 0.10.0, ", 
      "           : statsmodels 0.4.0", 
      "-------------------------------------------------------------------------------", 
      "", 
      "This notebook is a Python port of the R code in Chapter 2 of _Machine Learning", 
      "for Hackers_ by D. Conway and J.M. White.", 
      "", 
      "Running the notebook will produce 9 PNG figures and save them to the working ", 
      "directory.", 
      "", 
      "The height/weight dataset CSV file should be located in a /data/ subfolder of ", 
      "the working directory. ", 
      "", 
      "For a detailed description of the analysis and the process of porting it", 
      "to Python, see: slendrmeans.wordpress.com/will-it-python.", 
      "'''"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 4, 
       "text": [
        "&quot;&apos;\\n-------------------------------------------------------------------------------\\nFilename   : ch2.ipynb\\nDate       : 2012-04-30\\nAuthor     : C. Vogel\\nPurpose    : Replicate analysis of height and weight data in Chapter 2 of \\n           : _Machine Learning for Hackers_.\\nInput Data : 01_heights_weights_genders.tsv is available at the book&apos;s github \\n           : repository at https://github.com/johnmyleswhite/ML_for_Hackers.git\\nLibraries  : Numpy 1.6.1, Matplotlib 1.1.0, Pandas 0.7.3, scipy 0.10.0, \\n           : statsmodels 0.4.0\\n-------------------------------------------------------------------------------\\n\\nThis notebook is a Python port of the R code in Chapter 2 of _Machine Learning\\nfor Hackers_ by D. Conway and J.M. White.\\n\\nRunning the notebook will produce 9 PNG figures and save them to the working \\ndirectory.\\n\\nThe height/weight dataset CSV file should be located in a /data/ subfolder of \\nthe working directory. \\n\\nFor a detailed description of the analysis and the process of porting it\\nto Python, see: slendrmeans.wordpress.com/will-it-python.\\n&quot;"
       ]
      }
     ], 
     "prompt_number": 4
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "import numpy as np", 
      "from pandas import *", 
      "import matplotlib.pyplot as plt", 
      "import os", 
      "import statsmodels.api as sm", 
      "from statsmodels.nonparametric.kde import KDE", 
      "from statsmodels.nonparametric import lowess", 
      "from statsmodels.api import GLM, Logit"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 7
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Numeric Summaries", 
      "# p. 37"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 8
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Import the height and weights data", 
      "heights_weights = read_table('data/01_heights_weights_genders.csv', sep = ',', header = 0)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 9
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Assign the heights column to its own series, and describe it.", 
      "heights = heights_weights['Height']", 
      "heights.describe()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 10, 
       "text": [
        "count    10000.000000", 
        "mean        66.367560", 
        "std          3.847528", 
        "min         54.263133", 
        "25%         63.505620", 
        "50%         66.318070", 
        "75%         69.174262", 
        "max         78.998742"
       ]
      }
     ], 
     "prompt_number": 10
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Means, medians, and modes (p. 38)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 11
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "def my_mean(x):", 
      "    return float(np.sum(x)) / len(x)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 12
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "def my_median(x):", 
      "    '''", 
      "    Compute the median of a series x.", 
      "    '''", 
      "    ", 
      "    # Get a sorted copy of the values in the series (need to call values", 
      "    # otherwise integer indexing messes things up.)", 
      "    sorted_x = np.sort(x.values)", 
      "    if len(x) % 2 == 0:", 
      "        indices = [0.5 * len(x) - 1, 0.5 * len(x)]", 
      "        return np.mean(sorted_x[indices])", 
      "    else:", 
      "        # Ceil(x) - 1 = Floor(x), but this is to make clear that the -1 is to", 
      "        # account for 0-based counting.", 
      "        index = ceil(0.5 * len(x)) - 1", 
      "        return sorted_x.ix[index]"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 13
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Check my_mean and my_median against built-ins"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 14
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "my_mean(heights) - heights.mean()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 15, 
       "text": [
        "0.0"
       ]
      }
     ], 
     "prompt_number": 15
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "my_median(heights) - heights.median()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 16, 
       "text": [
        "0.0"
       ]
      }
     ], 
     "prompt_number": 16
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Quantiles (p. 40)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 17
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "heights.min(), heights.max()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 18, 
       "text": [
        "(54.263133325097101, 78.998742346389605)"
       ]
      }
     ], 
     "prompt_number": 18
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Range = max - min. Note: np.ptp(heights.values) will do the same thing.", 
      "# HT Nathaniel Smith"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 19
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "def my_range(s):", 
      "    '''", 
      "    Difference between the max and min of an array or Series", 
      "    '''", 
      "    return s.max() - s.min()", 
      "", 
      "my_range(heights)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 20, 
       "text": [
        "24.735609021292504"
       ]
      }
     ], 
     "prompt_number": 20
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Similarly, pandas doesn't seem to provide multiple quantiles. ", 
      "# But (1) the standard ones are available via .describe() and", 
      "# (2) creating one is simple."
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 21
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# To get a single quantile", 
      "heights.quantile(.5)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 22, 
       "text": [
        "66.31807008178464"
       ]
      }
     ], 
     "prompt_number": 22
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Function to get arbitrary quantiles of a series.", 
      "def my_quantiles(s, prob = (0.0, 0.25, 0.5, 1.0)):", 
      "    '''", 
      "    Calculate quantiles of a series.", 
      "", 
      "    Parameters:", 
      "    -----------", 
      "    s : a pandas Series ", 
      "    prob : a tuple (or other iterable) of probabilities at ", 
      "           which to compute quantiles. Must be an iterable,", 
      "           even for a single probability (e.g. prob = (0.50)", 
      "           not prob = 0.50).", 
      "", 
      "    Returns:", 
      "    --------", 
      "    A pandas series with the probabilities as an index.", 
      "    '''", 
      "    q = [s.quantile(p) for p in prob]", 
      "    return Series(q, index = prob)", 
      "", 
      "# With the default prob argument   ", 
      "my_quantiles(heights)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 23, 
       "text": [
        "0.00    54.263133", 
        "0.25    63.505620", 
        "0.50    66.318070", 
        "1.00    78.998742"
       ]
      }
     ], 
     "prompt_number": 23
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# With a specific prob argument - here deciles", 
      "my_quantiles(heights, prob = arange(0, 1.1, 0.1))"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 24, 
       "text": [
        "0.0    54.263133", 
        "0.1    61.412701", 
        "0.2    62.859007", 
        "0.3    64.072407", 
        "0.4    65.194221", 
        "0.5    66.318070", 
        "0.6    67.435374", 
        "0.7    68.558072", 
        "0.8    69.811620", 
        "0.9    71.472149", 
        "1.0    78.998742"
       ]
      }
     ], 
     "prompt_number": 24
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      " # Standard deviation and variances"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 25
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "def my_var(x):", 
      "    return np.sum((x - x.mean())**2) / (len(x) - 1)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 26
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "my_var(heights) - heights.var()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 27, 
       "text": [
        "-2.2854607095723622e-11"
       ]
      }
     ], 
     "prompt_number": 27
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "def my_sd(x):", 
      "    return np.sqrt(my_var(x))"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 28
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "my_sd(heights) - heights.std()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 29, 
       "text": [
        "-2.9700686354772188e-12"
       ]
      }
     ], 
     "prompt_number": 29
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Exploratory Data Visualization (p. 44)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 30
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Histograms"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 31
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# 1-inch bins", 
      "bins1 = np.arange(heights.min(), heights.max(), 1.)", 
      "heights.hist(bins = bins1, fc = 'steelblue')", 
      "plt.savefig('height_hist_bins1.png')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD7CAYAAACPDORaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
      }
     ], 
     "prompt_number": 32
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# 5-inch bins", 
      "bins5 = np.arange(heights.min(), heights.max(), 5.)", 
      "heights.hist(bins = bins5, fc = 'steelblue')", 
      "plt.savefig('height_hist_bins5.png')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD7CAYAAACPDORaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf1nkTMtfx1TGAAAAAElFTkSuQmCC\n"
      }
     ], 
     "prompt_number": 33
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# 0.001-inch bins", 
      "bins001 = np.arange(heights.min(), heights.max(), .001)", 
      "heights.hist(bins = bins001, fc = 'steelblue')", 
      "plt.savefig('height_hist_bins001.png')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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      }
     ], 
     "prompt_number": 122
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Kernel density estimators, from scipy.stats."
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 28
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Create a KDE ojbect", 
      "heights_kde = KDE(heights)", 
      "# Use fit() to estimate the densities. Default is gaussian kernel ", 
      "# using fft. This will provide a \"density\" attribute.", 
      "heights_kde.fit()"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 34
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Plot the density of the heights", 
      "# Sort inside the plotting so the lines connect nicely.", 
      "fig = plt.figure()", 
      "plt.plot(heights_kde.support, heights_kde.density)", 
      "plt.savefig('heights_density.png')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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UCgWA22frDxon6zIYDFi6dCnef/990VHIwbm4uODvf/87PvroI9FR6CFxzl1C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\ngFjuREQOiOVOROSA/h/OrOTXgP8ezwAAAABJRU5ErkJggg==\n"
      }
     ], 
     "prompt_number": 35
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Pull out male and female heights as arrays over which to compute densities", 
      "", 
      "heights_m = heights[heights_weights['Gender'] == 'Male'].values", 
      "heights_f = heights[heights_weights['Gender'] == 'Female'].values", 
      "heights_m_kde = KDE(heights_m)", 
      "heights_f_kde = KDE(heights_f)", 
      "heights_m_kde.fit()", 
      "heights_f_kde.fit()", 
      "", 
      "fig = plt.figure()", 
      "plt.plot(heights_m_kde.support, heights_m_kde.density, label = 'Male')", 
      "plt.plot(heights_f_kde.support, heights_f_kde.density, label = 'Female')", 
      "plt.legend()", 
      "plt.savefig('height_density_bysex.png')                "
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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J6clUnU6Hw4cPj664ewrnzl0kElHTDBmAirsFE9I0v9nZ2ZBKpfDx8Rnx50x2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\n5s2CuGvvFT5rAto6dThbVMd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uQmCC\n"
      }
     ], 
     "prompt_number": 36
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Do the same thing with weights.", 
      "weights_m = heights_weights[heights_weights['Gender'] == 'Male']['Weight'].values", 
      "weights_f = heights_weights[heights_weights['Gender'] == 'Female']['Weight'].values", 
      "weights_m_kde = KDE(weights_m)", 
      "weights_f_kde = KDE(weights_f)", 
      "weights_m_kde.fit()", 
      "weights_f_kde.fit()", 
      "", 
      "", 
      "fig = plt.figure()", 
      "plt.plot(weights_m_kde.support, weights_f_kde.density, label = 'Male')", 
      "plt.plot(weights_f_kde.support, weights_f_kde.density, label = 'Female')", 
      "plt.legend()", 
      "plt.savefig('weight_density_bysex.png')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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P34Z0zaopxMAHOoduzg7yxKl+WjwHDhxAXFwcBb4Rjz32GHbv3s13GcQEKPRt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\n9913fJcjKBT6ZtKu0yMt9zJ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      }
     ], 
     "prompt_number": 37
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Subplot weight density by sex.", 
      "fig, axes = plt.subplots(nrows = 2, ncols = 1, sharex = True, figsize = (9, 6))", 
      "plt.subplots_adjust(hspace = 0.1)", 
      "axes[0].plot(weights_f_kde.support, weights_f_kde.density, label = 'Female')", 
      "axes[0].xaxis.tick_top()", 
      "axes[0].legend()", 
      "axes[1].plot(weights_m_kde.support, weights_f_kde.density, label = 'Male')", 
      "axes[1].legend()", 
      "plt.savefig('weight_density_bysex_subplot.png')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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HMasPP/wQO3bswObNm0VHISJ6IBbrKdHr9UhJSUFmZiays7NNtu/duxdRUVEVtgkKCkJk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\nrkJggg==\n"
      }
     ], 
     "prompt_number": 38
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Scatter plot. Pull weight (both sexes) out as a separate array first, like ", 
      "# we did with height above."
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 39
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "weights = heights_weights['Weight']", 
      "plt.plot(heights, weights, '.k', mew = 0, alpha = .1)", 
      "plt.savefig('height_weight_scatter.png')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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EIvwehMNhfq20F5TOAqjzxinqa2trAMA9+sKrQwRdEP6P2fVlVDlSxU5Jh8BE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\n"
      }
     ], 
     "prompt_number": 40
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Lowess smoothing - this seems to be new functionality not yet in docs (as of 0.40, April 2012)."
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 41
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "lowess_line = lowess.lowess(weights, heights)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 46
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "plt.figure(figsize = (13, 9))", 
      "plt.plot(heights, weights, '.', mfc = 'steelblue', mew=0, alpha = .25)", 
      "plt.plot(lowess_line[:,0], lowess_line[:, 1],  '-', color = '#461B7E', label = \"Lowess fit\")", 
      "plt.legend(loc = \"upper left\")", 
      "plt.savefig('height_weight_lowess.png')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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cDPtEREQtrNFGTbW/m63aUDvd8Ro91xrqrYtjZS/52haTjQYAjWaprTP0svwG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\nLZ3dqU/P1k9Yey4W8dWSd9WcfRoNbGPD2fCLQFPsNzQ0NDT8TFhsAk4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yPwkpcjFpvrc3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\nq2r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eL9Ud1tk2VAbgMviXEtmZouFUXvTv39yjIVZ7/JnJjOjgAyCTl2lCMwO9nO9Bs05G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\n"
      }
     ], 
     "prompt_number": 48
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Logistic regression of sex on height and weight", 
      "# Sex is coded in the binary variable `male`."
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 49
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# LHS binary variable", 
      "male = (heights_weights['Gender'] == 'Male') * 1"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 50
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Matrix of predictor variables: hieght and weight from data frame", 
      "# into an Nx2 array.", 
      "hw_exog = heights_weights[['Height', 'Weight']].values"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 51
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Logit model 1: Using GLM and the Binomial Family w/ the Logit Link", 
      "# Note I have to add constants to the `exog` matrix. The prepend = True", 
      "# argument prevents a warning about future change to the default argument.", 
      "logit_model = GLM(male, sm.add_constant(hw_exog, prepend = True), family = sm.families.Binomial(sm.families.links.logit))", 
      "logit_model.fit().summary()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<table class=\"simpletable\">", 
        "<caption>Generalized Linear Model Regression Results</caption>", 
        "<tr>", 
        "  <th>Dep. Variable:</th>       <td>Gender</td>      <th>  No. Observations:  </th>  <td> 10000</td> ", 
        "</tr>", 
        "<tr>", 
        "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  9997</td> ", 
        "</tr>", 
        "<tr>", 
        "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>     2</td> ", 
        "</tr>", 
        "<tr>", 
        "  <th>Link Function:</th>        <td>logit</td>      <th>  Scale:             </th>    <td>1.0</td>  ", 
        "</tr>", 
        "<tr>", 
        "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -2091.3</td>", 
        "</tr>", 
        "<tr>", 
        "  <th>Date:</th>           <td>Tue, 08 May 2012</td> <th>  Deviance:          </th> <td>  4182.6</td>", 
        "</tr>", 
        "<tr>", 
        "  <th>Time:</th>               <td>14:24:49</td>     <th>  Pearson chi2:      </th> <td>9.72e+03</td>", 
        "</tr>", 
        "<tr>", 
        "  <th>No. Iterations:</th>         <td>8</td>        <th>                     </th>     <td> </td>   ", 
        "</tr>", 
        "</table>", 
        "<table class=\"simpletable\">", 
        "<tr>", 
        "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> ", 
        "</tr>", 
        "<tr>", 
        "  <th>const</th> <td>    0.6925</td> <td>    1.328</td> <td>    0.521</td> <td> 0.602</td> <td>   -1.911     3.296</td>", 
        "</tr>", 
        "<tr>", 
        "  <th>x1</th>    <td>   -0.4926</td> <td>    0.029</td> <td>  -17.013</td> <td> 0.000</td> <td>   -0.549    -0.436</td>", 
        "</tr>", 
        "<tr>", 
        "  <th>x2</th>    <td>    0.1983</td> <td>    0.005</td> <td>   38.663</td> <td> 0.000</td> <td>    0.188     0.208</td>", 
        "</tr>", 
        "</table>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 52, 
       "text": [
        "<class 'statsmodels.iolib.summary.Summary'>", 
        "\"\"\"", 
        "                 Generalized Linear Model Regression Results                  ", 
        "==============================================================================", 
        "Dep. Variable:                 Gender   No. Observations:                10000", 
        "Model:                            GLM   Df Residuals:                     9997", 
        "Model Family:                Binomial   Df Model:                            2", 
        "Link Function:                  logit   Scale:                             1.0", 
        "Method:                          IRLS   Log-Likelihood:                -2091.3", 
        "Date:                Tue, 08 May 2012   Deviance:                       4182.6", 
        "Time:                        14:24:49   Pearson chi2:                 9.72e+03", 
        "No. Iterations:                     8                                         ", 
        "==============================================================================", 
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]", 
        "------------------------------------------------------------------------------", 
        "const          0.6925      1.328      0.521      0.602        -1.911     3.296", 
        "x1            -0.4926      0.029    -17.013      0.000        -0.549    -0.436", 
        "x2             0.1983      0.005     38.663      0.000         0.188     0.208", 
        "==============================================================================", 
        "\"\"\""
       ]
      }
     ], 
     "prompt_number": 52
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Get the coefficient parameters.", 
      "logit_pars = logit_model.fit().params"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 53
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Logit model 2: Using the Logit function.", 
      "logit_model2 = Logit(male, sm.add_constant(hw_exog, prepend = True))", 
      "logit_model2.fit().summary()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Optimization terminated successfully.", 
        "         Current function value: 2091.297971", 
        "         Iterations 8"
       ]
      }, 
      {
       "html": [
        "<table class=\"simpletable\">", 
        "<caption>Logit Regression Results</caption>", 
        "<tr>", 
        "  <th>Dep. Variable:</th>      <td>Gender</td>      <th>  No. Observations:  </th>  <td> 10000</td> ", 
        "</tr>", 
        "<tr>", 
        "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  9997</td> ", 
        "</tr>", 
        "<tr>", 
        "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     2</td> ", 
        "</tr>", 
        "<tr>", 
        "  <th>Date:</th>          <td>Tue, 08 May 2012</td> <th>  Pseudo R-squ.:     </th>  <td>0.6983</td> ", 
        "</tr>", 
        "<tr>", 
        "  <th>Time:</th>              <td>14:24:51</td>     <th>  Log-Likelihood:    </th> <td> -2091.3</td>", 
        "</tr>", 
        "<tr>", 
        "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -6931.5</td>", 
        "</tr>", 
        "<tr>", 
        "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td> 0.000</td> ", 
        "</tr>", 
        "</table>", 
        "<table class=\"simpletable\">", 
        "<tr>", 
        "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> ", 
        "</tr>", 
        "<tr>", 
        "  <th>const</th> <td>    0.6925</td> <td>    1.328</td> <td>    0.521</td> <td> 0.602</td> <td>   -1.911     3.296</td>", 
        "</tr>", 
        "<tr>", 
        "  <th>x1</th>    <td>   -0.4926</td> <td>    0.029</td> <td>  -17.013</td> <td> 0.000</td> <td>   -0.549    -0.436</td>", 
        "</tr>", 
        "<tr>", 
        "  <th>x2</th>    <td>    0.1983</td> <td>    0.005</td> <td>   38.663</td> <td> 0.000</td> <td>    0.188     0.208</td>", 
        "</tr>", 
        "</table>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 54, 
       "text": [
        "<class 'statsmodels.iolib.summary.Summary'>", 
        "\"\"\"", 
        "                           Logit Regression Results                           ", 
        "==============================================================================", 
        "Dep. Variable:                 Gender   No. Observations:                10000", 
        "Model:                          Logit   Df Residuals:                     9997", 
        "Method:                           MLE   Df Model:                            2", 
        "Date:                Tue, 08 May 2012   Pseudo R-squ.:                  0.6983", 
        "Time:                        14:24:51   Log-Likelihood:                -2091.3", 
        "converged:                       True   LL-Null:                       -6931.5", 
        "                                        LLR p-value:                     0.000", 
        "==============================================================================", 
        "                 coef    std err          z      P>|z|      [95.0% Conf. Int.]", 
        "------------------------------------------------------------------------------", 
        "const          0.6925      1.328      0.521      0.602        -1.911     3.296", 
        "x1            -0.4926      0.029    -17.013      0.000        -0.549    -0.436", 
        "x2             0.1983      0.005     38.663      0.000         0.188     0.208", 
        "==============================================================================", 
        "\"\"\""
       ]
      }
     ], 
     "prompt_number": 54
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Get the coefficient parameters", 
      "logit_pars2 = logit_model2.fit().params"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Optimization terminated successfully.", 
        "         Current function value: 2091.297971", 
        "         Iterations 8"
       ]
      }
     ], 
     "prompt_number": 55
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Compare the two methods again. They give the same parameters.", 
      "DataFrame({'GLM' : logit_pars, 'Logit' : logit_pars2})"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\">", 
        "  <thead>", 
        "    <tr>", 
        "      <th></th>", 
        "      <th>GLM</th>", 
        "      <th>Logit</th>", 
        "    </tr>", 
        "    </thead>", 
        "    <tbody>", 
        "    <tr>", 
        "      <td><strong>const</strong></td>", 
        "      <td> 0.692543</td>", 
        "      <td> 0.692543</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <td><strong>x1</strong></td>", 
        "      <td>-0.492620</td>", 
        "      <td>-0.492620</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <td><strong>x2</strong></td>", 
        "      <td> 0.198340</td>", 
        "      <td> 0.198340</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 56, 
       "text": [
        "            GLM     Logit", 
        "const  0.692543  0.692543", 
        "x1    -0.492620 -0.492620", 
        "x2     0.198340  0.198340"
       ]
      }
     ], 
     "prompt_number": 56
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Draw a separating line in the [height, weight]-space.", 
      "# The line will separate the space into predicted-male", 
      "# and predicted-female regions."
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 57
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# Get the intercept and slope of the line based on the logit coefficients ", 
      "intercept = -logit_pars['const'] / logit_pars['x2']", 
      "slope =  -logit_pars['x1'] / logit_pars['x2']"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 58
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# Plot the data and the separating line", 
      "# Color code male and female points.", 
      "fig = plt.figure(figsize = (10, 8))", 
      "plt.plot(heights_f, weights_f, '.', label = 'Female', mew = 0, mfc='coral', alpha = .1)", 
      "plt.plot(heights_m, weights_m, '.', label = 'Male', mew = 0, mfc='steelblue', alpha = .1)", 
      "plt.plot(array([50, 80]), intercept + slope * array([50, 80]), '-', color = '#461B7E')", 
      "plt.legend(loc='upper left')", 
      "plt.savefig('height_weight_class.png')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
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AoRdQtY00BZcdU44hx6mWjDSilG39kjZCzs3/ZPI6a+IpyVo+yPdZEWQn1+8G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lRZthldnfDsf3yFH3znCi8+c5UoSHj4M2d44qkH+K9/9wt88qyFnS7EW+R0WbYtItaIXX28tBBz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\n8/kKp20K+awPRC3ro3JHtehrXj4nz6k/q14BvAjYu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      }
     ], 
     "prompt_number": 59
    }
   ]
  }
 ]
}